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41 lines
2.2 KiB
Markdown
41 lines
2.2 KiB
Markdown
# ADR 0001: Git-native Knowledge Base (Git as source of truth, Postgres as derived index)
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- **Status:** Proposed (brainstorm outcome — for team review)
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- **Date:** 2026-07-24
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- **Origin:** Rohan Verma's meeting proposal to pivot from the custom-built KB "file system" to a Git-based system due to persistent maintenance issues; Thierry Bakera to investigate.
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---
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## Context
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### What we have today
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SurfSense does **not** actually have a file system. It has a **virtual filesystem façade mapped onto Postgres rows**, used by the chat agent. The moving parts:
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- Virtual `/documents/` namespace computed from DB rows — `surfsense_backend/app/agents/chat/runtime/path_resolver.py`
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- Read-side backend faking `ls`/`read`/`glob`/`grep` over Postgres — `.../filesystem/backends/kb_postgres.py`
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- Write-side "commit at end of turn" layer — `.../main_agent/middleware/kb_persistence/middleware.py`
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- **Three separate hand-rolled versioning/audit systems:**
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- `DocumentVersion` (user history) — `app/utils/document_versioning.py`
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- `DocumentRevision` / `FolderRevision` (agent revert snapshots) — `app/services/revert_service.py`
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- `AgentActionLog` (tool-call audit)
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- Supporting machinery: fractional indexing for ordering, move tracking, `content_hash` change detection, chunk reconciliation.
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### The problem
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The team has been hand-implementing — on top of a relational DB never designed for it — the exact primitives Git provides natively. That re-implementation is the source of the "persistent maintenance issues."
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| Hand-rolled today | Git provides natively |
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|---|---|
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| `path_resolver` + folder tree | tree objects |
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| end-of-turn staged commit | atomic commits |
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| `DocumentVersion` snapshots | commit history |
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| `DocumentRevision` + `revert_service` | `git revert` / `reset` |
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| `AgentActionLog` | commit log / `blame` |
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| `content_hash` dedup | content-addressed blobs (SHA) |
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| fractional indexing / move tracking | tree diff / rename detection |
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### Search stack (unchanged by this ADR)
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Hybrid chunk search: pgvector (HNSW) + Postgres FTS + RRF, optional reranking. Chunking via Chonkie; incremental via `chunk_reconciler.py`. See `.../shared/retrieval/hybrid_search.py`.
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